FastAPI for model serving in ML Python - ML Experiment: Train & Evaluate

Experiment - FastAPI for model serving

Problem:You have a trained machine learning model and want to make it available so others can send data and get predictions quickly using a web API.

Current Metrics:The model works well locally with 90% accuracy, but there is no API to serve it for real-time predictions.

Issue:No model serving setup exists. Users cannot access the model predictions through a web service.

Your Task

Create a FastAPI web service that loads the trained model and returns predictions for input data. The API should respond within 200 milliseconds for a single prediction request.

Use FastAPI framework only.

Load the model once when the server starts.

Accept JSON input with feature values.

Return JSON output with prediction result.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

ML Python

from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import uvicorn

# Define input data schema
class InputData(BaseModel):
    feature1: float
    feature2: float
    feature3: float

app = FastAPI()

# Load model once at startup
model = joblib.load('model.joblib')

@app.post('/predict')
async def predict(data: InputData):
    features = [[data.feature1, data.feature2, data.feature3]]
    prediction = model.predict(features)[0]
    return {'prediction': int(prediction)}

if __name__ == '__main__':
    uvicorn.run(app, host='127.0.0.1', port=8000)

Created FastAPI app with POST /predict endpoint.

Defined InputData class for input validation.

Loaded the trained model once globally.

Returned prediction as JSON response.

Results Interpretation

Before: Model only runs locally, no API available.

After: FastAPI service provides predictions via HTTP POST requests with low latency.

Serving a machine learning model with FastAPI allows easy, fast, and scalable access to predictions over the web.

Bonus Experiment

Add input validation to reject requests with missing or invalid feature values and return clear error messages.

💡 Hint

Use Pydantic's built-in validation features and FastAPI's automatic error handling to manage invalid inputs.

Practice

1. What is the main purpose of using FastAPI in machine learning model serving?

easy

A. To create a web service that allows users to send data and get model predictions

B. To train machine learning models faster on GPUs

C. To visualize data with interactive charts

D. To store large datasets efficiently

Solution

1. Step 1: Understand FastAPI's role

   FastAPI is a web framework used to build APIs that can serve machine learning models.

2. Step 2: Identify the main use in ML serving

   It allows sending input data and receiving predictions via HTTP requests, making models accessible.

3. Final Answer:

   To create a web service that allows users to send data and get model predictions -> Option A

4. Quick Check:

   FastAPI serves ML models as web APIs = D [OK]

Hint: FastAPI is for serving models, not training or visualization [OK]

Common Mistakes:

• Confusing FastAPI with training libraries
• Thinking FastAPI is for data storage
• Assuming FastAPI is for data visualization

2. Which of the following is the correct way to define a POST endpoint in FastAPI for model prediction?

easy

A. app.post('/predict')

B. app.put('/predict')

C. app.get('/predict')

D. app.delete('/predict')

Solution

1. Step 1: Recall HTTP methods for sending data

   POST is used to send data to the server, suitable for prediction requests.

2. Step 2: Match method to FastAPI syntax

   FastAPI uses @app.post decorator to define POST endpoints.

3. Final Answer:

   app.post('/predict') -> Option A

4. Quick Check:

   POST endpoint for sending data = app.post [OK]

Hint: Use POST to send data for predictions in FastAPI [OK]

Common Mistakes:

• Using GET instead of POST for sending input data
• Confusing HTTP methods like PUT or DELETE
• Missing the @ decorator syntax

3. Given the following FastAPI code snippet, what will be the output when sending JSON {"text": "hello"} to /predict?

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    text: str

@app.post('/predict')
def predict(data: InputData):
    return {"length": len(data.text)}

medium

A. {"text": 5}

B. {"length": "hello"}

C. {"length": 5}

D. Error: Missing return type

Solution

1. Step 1: Understand input and output

   The input JSON has "text": "hello". The function returns the length of this string.

2. Step 2: Calculate length of 'hello'

   "hello" has 5 characters, so the output JSON is {"length": 5}.

3. Final Answer:

   {"length": 5} -> Option C

4. Quick Check:

   len('hello') = 5 [OK]

Hint: Count characters in input string for length output [OK]

Common Mistakes:

• Returning the string instead of its length
• Confusing keys in the output JSON
• Expecting a syntax or runtime error

4. Identify the error in this FastAPI model serving code:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class InputData(BaseModel):
    value: int

@app.post('/predict')
def predict(data: InputData):
    return {"result": data.value * 2}

# Client sends JSON: {"value": "10"}

medium

A. Missing return statement

B. Type error because 'value' should be int but client sent string

C. Syntax error in function definition

D. No error, code works fine

Solution

1. Step 1: Check data type in Pydantic model

   InputData expects 'value' as int, client sends string "10".

2. Step 2: Understand Pydantic validation and coercion

   Pydantic automatically converts compatible strings like "10" to int 10, so no validation error occurs.

3. Final Answer:

   No error, code works fine -> Option D

4. Quick Check:

   Pydantic coerces "10" to 10 [OK]

Hint: Pydantic automatically converts compatible types like str to int [OK]

Common Mistakes:

• Thinking Pydantic doesn't auto-convert string to int
• Expecting a type validation error
• Overlooking Pydantic's coercion feature

5. You want to serve a sentiment analysis model using FastAPI. The model expects a list of texts and returns a list of sentiment scores. Which is the best way to define the input and output in FastAPI?

hard

A. Accept raw JSON string and return a plain string

B. Use a Pydantic model with a field 'texts' as List[str] and return List[float]

C. Use query parameters for each text input and return a single float

D. Use a Pydantic model with a single string field and return a dictionary with one score

Solution

1. Step 1: Define input for multiple texts

   Since the model expects multiple texts, input should be a list of strings, best defined with Pydantic List[str].

2. Step 2: Define output for multiple scores

   The model returns multiple sentiment scores, so output should be a list of floats matching input size.

3. Final Answer:

   Use a Pydantic model with a field 'texts' as List[str] and return List[float] -> Option B

4. Quick Check:

   Input/output types match model expectations [OK]

Hint: Match input/output types to model's batch processing [OK]

Common Mistakes:

• Using single string input for multiple texts
• Returning single score instead of list
• Using query params for large input lists